Big Data is among the biggest IT trends of the last five years. The idea behind this trend is that given a sufficiently large volume of data, it is possible to derive crucial business insights that could not be discovered through other methods.

Of course, running Big Data analytics at scale requires a significant amount of processing power, and tools for parsing the data. While you can do this on premise, it is not always feasible to set up and maintain a sufficiently large infrastructure. That is why several of the leading cloud service providers have begun offering solutions for processing large volumes of data via Hadoop clusters, or similar solutions. Some of the providers offering such solutions include Amazon Web Services (AWS), Microsoft Azure, and Alibaba Cloud.

This article compares the major cloud-based Big Data platforms offered by each of these public cloud providers.

Hadoop Basics

For those who might not be familiar, Hadoop is an open source framework that is designed to run distributed applications across nodes in a cluster. One of the key differences between a Hadoop cluster and other types of clusters (such as a Microsoft failover cluster) is that Hadoop is designed specifically for use with data-intensive applications.

Hadoop clusters utilize a technique called MapReduce. MapReduce works by breaking a data analytics job into numerous fragments, which are then distributed across the cluster’s nodes for execution.

AWS EMR

Amazon Web Services’ solution for Big Data analytics is AWS EMR. Amazon EMR uses a series of EC2 virtual machine instances to form an Apache Hadoop cluster. A cluster can contain as many as 20 EC2 instances, and Amazon gives subscribers the option of creating multiple Hadoop clusters.

Amazon EMR is designed to work with Hive, Impala, Pig, HBase, and Kinesis Connector. The data that is to be analyzed by the AWS EMR service is stored on Amazon S3 storage. S3 storage can be accessed programmatically through an API, but administrators also have the option of using any of the available third-party S3 clients to upload the data to Amazon EMR.

Azure HDInsight

Microsoft’s Big Data analytics solution within the Azure cloud is Azure HDInsight. HDInsight uses Azure virtual machine instances to create clusters for Hadoop, Spark, Hive, HBase, Storm, Kafka, and Microsoft R Server. The service is designed to work with a variety of development environments, including Microsoft’s Visual Studio, and third-party solutions such as Eclipse and IntelliJ. The service is also designed to work with the Jupyter and Zeppelin notebooks.

Azure HDInsight is designed to be as flexible as possible. When creating a cluster, administrators can choose from any of the Azure virtual machine types, thereby allowing the cluster capabilities to be closely matched to the requirements of the job, while helping to control costs. It is also worth noting that Microsoft provides a 99.9% SLA that extends end-to-end, across the entire workload (not just the VMs that form the cluster).

Azure HDInsight is designed to integrate with Azure Active Directory, and supports multi-factor authentication. The service is also compliant with the requirements of the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry (PCI).

Alibaba Cloud E-MapReduce

Alibaba Cloud’s data analytics solution is E-MapReduce. Like some competing solutions, Alibaba Cloud E-MapReduce is based around the use of a Hadoop cluster. However, Alibaba Cloud does not use a one-size- fits-all solution. Subscribers are able to choose the ECS model that they wish to use (CPU or Memory), and then the entire cluster is created within a matter of minutes. Once online, the Hadoop cluster is able to dynamically add nodes on an as needed basis. Even so, administrators have the ability to configure and tune the cluster as required.

Alibaba Cloud E-MapReduce is designed to work with the Apache Spark, MapReduce, and Apache Pig frameworks. The data that is being analyzed can be stored on Apache HDFS or HBase, and the cluster supports the use of tools such as Apache Sqoop and Spark SQL. Furthermore, workloads can be scheduled to run automatically. Alibaba Cloud E-MapReduce supports a variety of computational processes, including machine learning, process orchestration, stream processing, and even graph analytics.

Alibaba Cloud enforces data security through a role-based access control mechanism in which a primary account has the option of creating additional accounts, and granting specific service permissions to those accounts. Additionally, the data that is to be analyzed can be encrypted, and the ECS instances that make up the cluster can be protected with a firewall.

Conclusion

The success of an organization’s Big Data analytics initiative hinges on having the right computational tools for the job. A variety of public cloud service providers offer MapReduce services that can help with analytics. However, these services are not created equally. When selecting a cloud-based MapReduce service, it is important to consider things like platform compatibility and integration, security, and of course, overall flexibility.